1. Field of the Invention
The present invention is directed generally to a system and method for detecting moving vehicles on roadways and, more specifically, to a neural network-based system and method for vehicle detection.
2. Description of the Background of the Invention
Vehicle detection on roadways is useful for a variety of traffic engineering applications from intersection signal control to transportation planning. Traditional detection methods have relied on mechanical or electrical devices placed on top of, or embedded in, roadway pavements. These systems are relatively expensive to install, tend to be unreliable over time and are limited in their abilities.
Considerable research has been conducted in the area of machine vision for Wide Area Vehicle Detection Systems (WADS). One noteworthy vision-based traffic sensor was developed in a cooperative effort between the Belgian government, Leuven University and Devlonics Control. This system is currently marketed as CCATS (Camera and Computer Aided Traffic Sensor) by Devlonics Control. Another WADS developed by the Metropolitan Expressway of Japan is used to measure traffic volume, average speed and space occupancy. The University of Sheffield in the United Kingdom has also conducted research on automatic vehicle recognition using a special purpose image processing machine (RAPAC) developed at the university. Researchers in France have developed a prototype image-based traffic measurement system called TITAN. This system is designed to measure volumes, speed and occupancy on multi-lane freeways under light traffic conditions. Under heavy traffic conditions, it is only capable of measuring occupancy. Additional information concerning these systems can be found in Darcy Bullock et al., A Prototype Neural Network for Vehicle Detection, Proceedings of the Artificial Neural Networks in Engineering (ANNIE '91) Conference (held Nov. 10--13, 1991) (ASME Press, New York, N.Y. 1991) which is hereby incorporated by reference.
However, these systems have typically employed conventional image processing and pattern matching algorithms which often require large amounts of computing resources. In addition, many installations have been sensitive to varying lighting conditions, camera perspectives and shadows.
Thus, the need exists for a system designed to detect passing vehicles on a roadway which is inexpensive to install, is reliable over time and has the capability to detect vehicles located in various positions on the roadway. Moreover, such a system must not require large amounts of computing resources and must be relatively insensitive to varying lighting conditions, camera perspectives and shadow conditions.